Jonmohamadi Yaqub, Poudel Govinda, Innes Carrie, Jones Richard
Department of Medicine, University of Otago, Christchurch, New Zealand,
Australas Phys Eng Sci Med. 2014 Jun;37(2):457-64. doi: 10.1007/s13246-014-0265-x. Epub 2014 Apr 6.
In electroencephalography (EEG) and magnetoencephalography signal processing, scalar beamformers are a popular technique for reconstruction of the time-course of a brain source in a single time-series. A prerequisite for scalar beamformers, however, is that the orientation of the source must be known or estimated, whereas in reality the orientation of a brain source is often not known in advance and current techniques for estimation of brain source orientation are effective only for high signal-to-noise ratio (SNR) brain sources. As a result, vector beamformers are applied which do not need the orientation of the source and reconstruct the source time-course in three orthogonal (x, y, and z) directions. To obtain a single time-course, the vector magnitude of the three orthogonal outputs of the beamformer can be calculated at each time point (often called neural activity index, NAI). The NAI, however, is different from the actual time-course of a source since it contains only positive values. Moreover, in estimating the magnitude of the desired source, the background activity (undesired signals) in the beamformer outputs also become all positive values, which, when added to each other, leads to a drop in the SNR. This becomes a serious problem when the desired source is weak. We propose applying independent component analysis (ICA) to the orthogonal time-courses of a brain voxel, as reconstructed by a vector beamformer, to reconstruct the time-course of a desired source in a single time-series. This approach also provides a good estimation of dipole orientation. Simulated and real EEG data were used to demonstrate the performance of voxel-ICA and were compared with a scalar beamformer and the magnitude time-series of a vector beamformer. This approach is especially helpful when the desired source is weak and the orientation of the source cannot be estimated by other means.
在脑电图（EEG）和脑磁图信号处理中，标量波束形成器是一种用于在单个时间序列中重建脑源时间进程的常用技术。然而，标量波束形成器的一个前提条件是必须知道或估计源的方向，而在实际中，脑源的方向通常事先并不清楚，并且当前用于估计脑源方向的技术仅对高信噪比（SNR）的脑源有效。因此，应用了矢量波束形成器，它不需要源的方向，并在三个正交（x、y和z）方向上重建源时间进程。为了获得单个时间进程，可以在每个时间点计算波束形成器三个正交输出的矢量大小（通常称为神经活动指数，NAI）。然而，NAI与源的实际时间进程不同，因为它只包含正值。此外，在估计所需源的大小时，波束形成器输出中的背景活动（不需要的信号）也都变为正值，将它们相加会导致SNR下降。当所需源较弱时，这会成为一个严重的问题。我们建议将独立成分分析（ICA）应用于由矢量波束形成器重建的脑体素的正交时间进程，以在单个时间序列中重建所需源的时间进程。这种方法还能很好地估计偶极子方向。使用模拟和真实的EEG数据来证明体素ICA的性能，并与标量波束形成器和矢量波束形成器的大小时间序列进行比较。当所需源较弱且无法通过其他方法估计源的方向时，这种方法特别有用。
